-- thanks to https://github.com/soumith/imagenet-multiGPU.torch for this example
require 'nn'
local nGPU = 4
local nClasses = 10
local modelType = 'A' -- on a titan black, B/D/E run out of memory even for batch-size 32

-- Create tables describing VGG configurations A, B, D, E
local cfg = {}
if modelType == 'A' then
  cfg = {64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'}
elseif modelType == 'B' then
  cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'}
elseif modelType == 'D' then
  cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'}
elseif modelType == 'E' then
  cfg = {64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'}
else
  error('Unknown model type: ' .. modelType .. ' | Please specify a modelType A or B or D or E')
end

local features = nn.Sequential()
do
  local iChannels = 3;
  for k,v in ipairs(cfg) do
     if v == 'M' then
        features:add(nn.SpatialMaxPooling(2,2,2,2))
     else
        local oChannels = v;
        local conv3 = nn.SpatialConvolution(iChannels,oChannels,3,3,1,1,1,1);
        features:add(conv3)
        features:add(nn.ReLU(true))
        iChannels = oChannels;
     end
  end
end

-- features:cuda()
-- features = makeDataParallel(features, nGPU) -- defined in util.lua

local classifier = nn.Sequential()
classifier:add(nn.View(512*7*7))
classifier:add(nn.Linear(512*7*7, 4096))
classifier:add(nn.Threshold(0, 1e-6))
classifier:add(nn.Dropout(0.5))
classifier:add(nn.Linear(4096, 4096))
classifier:add(nn.Threshold(0, 1e-6))
classifier:add(nn.Dropout(0.5))
classifier:add(nn.Linear(4096, nClasses))
classifier:add(nn.LogSoftMax())
-- classifier:cuda()

local model = nn.Sequential()
model:add(features):add(classifier)
model.imageSize = 256
model.imageCrop = 224

return model
